Method to identify microorganisms using spectroscopic technique

ABSTRACT

A method to classify and identify microorganisms using the FTIR ATR technique.

FIELD OF THE INVENTION

The present invention concerns a method to identify microorganisms usingstatistical techniques, such as for example the technique ofmultivariate analysis of spectral profiles, or alternatively neuralnetworks, to obtain indications relating to the growth of microorganismsin extremely short times compared to conventional methods.

In particular, the invention is based on the development of ananalytical instrument to identify microorganisms through the analysis ofthe spectral profile ATR-FTIR of an unknown sample, and the comparisonof the spectral profile with those of samples previously collected andmemorized in a database.

BACKGROUND OF THE INVENTION

Fourier transform infrared spectroscopy (FTIR) is a non-destructiveanalysis technique, which allows to obtain information on the chemicalcomposition of a sample analyzed. Since the beginning of the 1990s thistechnique has been used for the analysis of biological samples (Diem M.et al., The Analyst [27 Aug. 2004, 129 (10): 880-885]).

At the same time, the ability of the FTIR technique to identify andclassify unknown microorganisms was shown (Helm D. et al., Journal ofGeneral Microbiology (1991, 137, 69-79) and Marley L. et al.,Vibrational Spectroscopy 26, 2, 2001, 151-159).

The different species, subspecies or sub-classifications ofmicroorganisms are characterized by a precise biochemical composition,in terms of proteins, lipids, nucleic acids and polysaccharides, whichis reflected in a distinct vibrational spectrum (ed. Griffiths andChalmers, 2001 Handbook of vibrational spectroscopy, John Wiley & sons,New York, Volume 5).

Some potential applications suggested by the FTIR technique inmicrobiology include:

(i) identification of pathogens in the human or veterinary field, forexample in a clinical laboratory;

(ii) epidemiological investigations, topic of study, screening ofpathogens, hygiene checks, elucidation of infectious chains, controltherapies and detection of recurrent infections;

(iii) characterization and screening of microorganisms from theenvironment;

(iv) monitoring of biotechnological processes;

(v) microbiological quality control in food or pharmaceuticalindustries;

(vi) maintenance of harvested strains.

Methods to identify microorganisms are known, based on techniques ofcomparative analysis between the spectrum of an unknown sample and thespectra of known species of microorganisms, stored in a database.

Examples of known identification methods of this type are reported indocuments U.S. Pat. No. 5,660,998A, CN103217398A, U.S. Pat. No.6,379,920B1, WO2006002537A1, U.S. Pat. No. 9,551,654B2, US20170167973A1,WO2017/210783A1, Sousa et al., European Journal of Clinical Microbiology& Infectious Diseases (2014) 33: 1345-1353, Whittaker et al., Journal ofMicrobiological Methods 55 (2003) 709-716, Wang et al., InternationalJournal of Food Microbiology 167 (2013) 293-302.

In many of the known methods, however, spectra acquisition is performedin transmission, reflection, or imaging modes.

For example, transmission or reflection acquisition modes are reportedin U.S. Pat. Nos. 5,660,998 and 6,379,920, while imaging modes areadopted in WO2006002537A1, U.S. Pat. No. 9,551,654B2, US20170167973A1.

These methods have disadvantages connected to the fact that the signalobtained directly depends on the thickness and morphology of the sample,which may be too thick and generate saturation, or non-homogeneous andgenerate distortions due to scattering.

It is also known that methods based on imaging approaches, for examplemultipixel, may have limitations on the resolution of individualspectra, due to the lower signal to noise ratio obtainable per pixel,compared to single-point detectors.

Another disadvantage of the state of the art is that often the spectracan present signals due to the presence of water or other contaminants,which cover the peculiar signals of microorganisms.

In some cases, such as for example CN103217398A, drying procedures areadopted but, as reported in WO 2017/210783A1, these procedures can havesignificant and irreversible effects on the microorganisms, alsomodifying their spectra.

In some cases, such as for example WO 2017/210783 A1, it is possible toresort to multiple acquisitions to remove the water components presentin the spectra. However, this solution has disadvantages connected togreater analysis time and greater complexity of the analyticalprocedures for comparing spectra.

Another disadvantage of the state of the art is that often the referencedatabase comprises a limited number of species of microorganisms, andtherefore does not cover a wide range of possible species.

By way of example, the database and the related identification methodreported in CN103217398A refer to 13 bacterial species.

Moreover, with the increase in sizes of the reference databases, aseries of problems relating to the procedures for cultivating andgrowing the species of microorganisms comes into being, to theprocedures for acquiring the spectra and to the comparative analysisprocedures.

For example, as the size of the database increases, the analysis time ofthe unknown sample also increases, given that the spectrum of theunknown sample must be compared with a large number of spectra containedin the database.

Moreover, the same species of microorganisms can have a greatvariability in the spectral characteristics, making it difficult tocompare them with the spectra contained in the database.

This variability may be due to the presence of different strains foreach species, or to differences between different cultures of the samespecies due to possible variations in the chemical composition of theculture medium and/or growth conditions.

There is therefore the need to develop new methods to identifymicroorganisms that can work for a large number of species, subspeciesor sub-classifications of microorganisms, and with a great variabilityof the reference database.

One purpose of the present invention is therefore to develop a method toidentify microorganisms that is general enough to be able to identify alarge number of different species, subspecies or sub-classifications ofmicroorganisms, with high capacity for discrimination and precision.

Another purpose of the present invention is to develop a method toidentify microorganisms that can identify different strains of the samespecies, subspecies or sub-classification of microorganisms, possiblygrown in different cultures on different media, or in differentenvironmental conditions in the presence or absence of biologicalfluids.

Another purpose of the present invention is to provide a method toidentify microorganisms that is accurate, but that also requires rapidtimes for each individual analysis, even in the case of large databases.

Another purpose of the present invention is to provide an apparatuswhich can implement the method to identify microorganisms in a simplemanner, integrating all the different steps of analysis and instrumentalcomponents.

The Applicant has devised, tested and embodied the present invention toovercome the shortcomings of the state of the art and to obtain theseand other purposes and advantages.

SUMMARY OF THE INVENTION

The present invention is set forth and characterized in the independentclaims, while the dependent claims describe other characteristics of theinvention or variants to the main inventive idea.

The present invention concerns a method to identify microorganisms, andin particular a method to allow the identification of several species,subspecies and sub-classifications of microorganisms present in anunknown sample, comparing the infrared spectrum of said unknown samplewith infrared spectra of known samples, which have been previouslyarchived in a database.

The method to identify microorganisms according to the present inventioncomprises:

-   -   a step of preparing a database of reference spectra associated        with known samples of species, subspecies and        sub-classifications of known microorganisms;    -   a step of creating pre-calculated models, or reference        libraries, starting from said database, based on the most        significant identification spectral characteristics, for example        associated with information on shapes, sizes, intensity and peak        areas of absorption, or also correlations and ratios between the        intensities or between the different peak areas;    -   one or more steps of sampling an unknown sample, taken from the        patient, possibly subsequently grown on solid or liquid media        and possibly also comprising biological fluids;    -   one or more steps of acquiring the spectrum of the unknown        sample;    -   one or more steps of processing the spectrum of the unknown        sample;    -   one or more steps of analyzing the spectrum of the unknown        sample, by comparing it with the pre-calculated models, each of        which provides at least some belonging scores, possibly in the        form of percentages, of the unknown sample to one or more of        said species, subspecies or sub-classifications of known        microorganisms, and a parameter of the reliability of said        belonging scores;    -   one or more control steps in which, starting from said belonging        scores and from said reliability parameter, a new acquisition        step of the spectrum of the unknown sample is requested, or a        final result is provided, which can comprise a failed        identification, or a successful identification.

In some embodiments, the step of preparing a database of referencespectrums associated with known sample species, subspecies orsub-classification of known microorganisms provides, for each sample,also comprises:

-   -   a step of sampling a known sample, possibly taken from patients,        possibly subsequently grown on solid or liquid media and        possibly comprising biological fluids;    -   one or more steps of acquiring the spectrum of the known sample;    -   one or more steps of processing the spectrum of the known        sample.

In some embodiments, the step of preparing the database can be carriedout only once to create the database, which can then be used for eachsubsequent analysis of unknown samples.

In some embodiments, the step of preparing the database can also referto the database update, to be carried out whenever it is desired toinclude new species, subspecies or sub-classifications ofmicroorganisms, thus extending the range of applicability of the method.

Similarly, the pre-calculated models, once created, can be used forevery subsequent analysis of unknown samples, or can also be updatedevery time it is desired to add new species, subspecies orsub-classifications of microorganisms.

According to the present invention, the spectra are acquired by means ofan apparatus comprising an FTIR-ATR spectrophotometer.

This acquisition mode has the advantage that it obtains spectraindependent of the thickness and morphology of the sample, guaranteeinga high reproducibility compared with techniques based on transmittanceor reflectance.

Moreover, it allows to operate on an extremely small sample, and withextremely simple and fast operating procedures.

In some embodiments of the present invention, the FTIR-ATRspectrophotometer comprises a single-point detector, which guarantees abetter signal-to-noise ratio than, for example, multipixel and/orimaging-based methods.

This characteristic also allows to limit or prevent phenomena of signalscattering and saturation and to guarantee a simpler preparation of thesample.

In some embodiments, the spectrophotometer has a continuous acquisitionmode, which facilitates the cleaning of the instrument and also allowsto diagnose in real time the presence of water and/or othercontaminants, allowing to limit the disadvantage of the state of the artwhereby the presence of water and/or contaminants can cover the peculiarsignals of the microorganisms.

Advantageously, according to the present invention, spectroscopicmonitoring of the drying level of the sample is provided, so that thespectrum is acquired only when a predetermined standard level of dryinghas been reached.

The spectra acquired with this mode therefore refer to samples that havesubstantially the same levels of drying, and are therefore morecomparable to each other.

This characteristic allows to overcome, or at least to limit, somedisadvantages of the state of the art, since no oven drying operationsare necessary to remove water from the sample, thus avoiding theoccurrence of irreversible effects on the microorganisms and thereforeon the corresponding spectra.

In some embodiments, the present invention provides to use algorithmswhich automatically identify the most significant identificationspectral characteristics of the samples in spectral ranges establishedin advance.

This characteristic allows to significantly reduce the time required bythe analysis step, allowing to speed up the times and increase theaccuracy of the identification.

Advantageously, the use of said pre-calculated models allows to speed upthe identification times for each unknown sample, compared with methodsknown in the state of the art.

These characteristics allow to increase the size of the database,allowing to identify many species, subspecies or sub-classifications ofdifferent microorganisms, of different strains of the same species,subspecies or subfamily, also possibly grown in different cultures ondifferent media, or in different environmental conditions, possibly inthe presence of biological fluids.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other aspects, characteristics and advantages of the presentdisclosure will be understood with reference to the followingdescription, drawings and attached claims. The drawings, which are anintegral part of the description, show some embodiments of the presentinvention and, together with the description, propose to describe theprinciples of the disclosure.

The present invention is described and shown better with the aid of thefollowing drawings, in which:

FIG. 1 is a schematic representation of a possible apparatus inaccordance with the method of the present invention;

FIG. 2 shows a block diagram in which the steps of one embodiment of themethod of the present invention are shown by way of example;

FIGS. 3a and 3b show experimental data collected using the FTIR-ATRtechnique, where FIG. 3a shows the raw data collected, wherein eachspecies is represented by a different color, while FIG. 3b shows thedata transformed;

FIG. 4 schematically shows the zones of the clusters obtained using PCAin the space of two principal components, corresponding to the spectraof different species of microorganisms;

FIG. 5 schematically shows an example of zones of the clusters obtainedusing LDA in the space of the first two components;

FIG. 6 schematically shows an example of zones of the clusters obtainedusing PCA in the space of two principal components, corresponding to thespectra of different species of microorganisms;

FIG. 7 shows: panel a), representation of the PCA space colored aspositive GRAM (light gray) and negative GRAM (dark gray); panel b),confusion matrix showing the “Predicted” data with respect to the“Actual” data obtained by the method; panel c), results of theperformance of the mathematical method applied to the embodimentsdescribed here in predicting the GRAM type;

FIG. 8 is a confusion matrix obtained by the validation through crossvalidation of the method according to the embodiments described herewith the spectra of the database.

To facilitate comprehension, the same reference numbers have been used,where possible, to identify identical common elements in the drawings.It is understood that elements and characteristics of one embodiment canconveniently be incorporated into other embodiments without furtherclarifications.

DETAILED DESCRIPTION OF SOME EMBODIMENTS

We will now refer in detail to the various embodiments of the presentinvention, of which one or more examples are shown in the attacheddrawings. Each example is supplied by way of illustration of theinvention and shall not be understood as a limitation thereof. Forexample, the characteristics shown or described insomuch as they arepart of one embodiment can be adopted on, or in association with, otherembodiments to produce another embodiment. It is understood that thepresent invention shall comprise all such modifications and variants.

Before describing these embodiments, we must also clarify that thepresent description is not limited in its application to details of theconstruction and disposition of the components as described in thefollowing description using the attached drawings. The presentdescription can provide other embodiments and can be obtained orexecuted in various other ways. We must also clarify that thephraseology and terminology used here is for the purposes of descriptiononly, and cannot be considered as limitative.

Unless otherwise defined, all the technical and scientific terms usedhere and hereafter have the same meaning commonly understood by a personof ordinary experience in the field to which the present inventionbelongs.

Although methods and materials similar or equivalent to those describedhere may be used in practice or in the tests to verify this disclosure,the methods and materials are described below by way of example. In theevent of conflict, the present application, comprising the definitions,shall prevail. The materials, methods and examples are purelyillustrative and must not be understood in a restrictive manner.

Here and hereafter, by sample we mean a minimum quantity taken from alarger homogeneous set of a biological substance containing microbialorganisms, such as for example microorganisms, but not only, foranalysis purposes.

Sometimes, if necessary, we will specify unknown sample or known sample,in cases where the composition of microorganisms of the biologicalsubstance is, respectively, unknown or known.

In some embodiments, the species, subspecies and sub-classifications ofmicroorganisms may be of clinical interest.

Moreover, the term “spectrum” will be used with reference to the set ofelectromagnetic radiations compatible with the acquisition instrumentused, and therefore the terms “infrared spectrum”, “vibrationalspectrum” or references to vibrational transitions or certain particularspectral ranges must not be understood in a restrictive sense withrespect to the applicability of the method according to the presentinvention.

FIG. 1 schematically shows an apparatus 10 to identify microorganisms inaccordance with the present invention.

By way of example, the apparatus comprises a spectrophotometer for FTIRspectroscopy (Fourier Transform Infra-Red) in ATR (Attenuated TotalReflectance) acquisition mode, that is, a FTIR-ATR spectrophotometer,coupled with a processing system and a data display system.

The processing system and the display system can, for example, beintegrated into a single processing and display system and/or can becomprised in a computer 15, such as for example a personal computerequipped with a screen.

In some embodiments, the processing system comprises a computer program,which can be memorized in a computer-readable medium and which containsinstructions that can be executed on each occasion by the apparatus 10.

Hereafter, for simplicity of exposition, reference will be made to thecomputer 15 to indicate the set of software and hardware systems able tomanage the apparatus 10, and to process and display the data.

The FTIR-ATR spectrophotometer, known per se in its main components,comprises a source 13 of infrared radiation 17, reflecting elements 16,an internal reflection element and a detector 14.

For simplicity, other components of the spectrophotometer, such asmono-chromator, chopper, interferometer, have not been shown in FIG. 1.

In some embodiments, the source 13 can be a black body source of theGlobar type.

By way of example, the radiation 17 emitted by the source 13, that is,the incident radiation 17 a, is directed by the reflecting elements 16to hit the internal reflection element.

In some embodiments, the internal reflection element can be, forexample, a crystal 12.

In some embodiments, the crystal 12 can be a crystal 12 with a highrefraction index.

In some embodiments, the crystal 12 can be a crystal 12 of diamond,ZnSe, silicon or germanium.

Advantageously, if the crystal 12 is a diamond crystal 12, it has theadvantages of being more durable than ZnSe, silicon or germanium, usedin comparable measurements, and of having a greater transparency range.

The reflection of the radiation 17 inside the crystal 12 produces anevanescent field on the surface of the crystal 12, on which anacquisition zone 18 can be defined, on which the biological sample to beanalyzed is positioned.

The evanescent field can penetrate for a range of depths that, dependingon the case, can reach up to a few microns inside the sample.

In particular, this range of depths is a function of the angle ofincidence and the wavelength of the incident radiation 17 a, as well asof the refractive index of the material used for the crystal 12, andtherefore can be considered substantially constant for all the samplesanalyzed.

For this reason, advantageously, the ATR mode allows to acquire spectraindependent of the optical path of radiation through the sample, andtherefore independent of the thickness of the sample, guaranteeing ahigher reproducibility compared to approaches that use transmission orreflectance.

Moreover, this mode allows to limit or prevent phenomena of scatteringand/or signal saturation that can negatively affect the quality of thespectrum.

This mode therefore guarantees greater repeatability of the measurementand a simpler preparation of the sample.

The radiation 17 exiting from the crystal 12 after the interaction withthe sample, that is, the outgoing radiation 17 b, is then directed bythe reflecting elements 16 toward a detector 14.

In some embodiments, the detector 14 can be a DLaTGS detector(Deuterated L-alanine doped triglycine sulphate).

In alternative embodiments, the detector 14 can be a DTGS detector.

By way of example, the detector 14 transforms the optical informationcontained in the outgoing radiation 17 b into electric signals, whichare sent to the computer 15.

In some embodiments, the apparatus is disposed to acquire spectra in theregion NIR (Near IR) and/or MIR (Mid IR) and/or FIR (Far IR).

In some embodiments, the apparatus 10 can operate in continuousacquisition mode, that is, displaying on the screen of the computer 15in real time what is acquired on each occasion in the acquisition zone18.

The present invention also concerns a method to identify microorganisms,shown schematically in FIG. 2 and comprising:

-   -   a step of preparing a database (A1, A2, A3) of reference spectra        associated with known samples of species, subspecies and        sub-classifications of known microorganisms;    -   a step A4 of creating pre-calculated models, starting from said        database, based on identification spectral characteristics most        significant for each species, subspecies or sub-classification        of known microorganisms;    -   a step B of sampling of an unknown sample, taken from the        patient, possibly grown subsequently on solid or liquid media        and possibly comprising biological fluids;    -   one or more steps C of acquiring the spectrum of the unknown        sample;    -   one or more steps D of processing the spectrum of the unknown        sample;    -   one or more steps of analyzing the spectrum of the unknown        sample, by comparison with the pre-calculated models, each of        which analysis steps provides at least some scores, possibly in        the form of percentages of the unknown sample belonging to one        or more of said species, subspecies or sub-classifications of        known microorganisms and a parameter of the reliability of said        belonging scores;    -   one or more control steps F in which, starting from said        belonging scores and from said reliability parameter, a new        acquisition step of the spectrum C of the unknown sample is        requested, or a final result of the method is provided, which        can comprise a failed identification J, or a successful        identification G, H, I according to different criteria.

In some embodiments of the present invention, the step of preparing adatabase A can in turn comprise:

-   -   a step A1 of sampling a known sample, possibly taken from        patients, possibly subsequently grown on solid or liquid media        and possibly comprising biological fluids;    -   one or more steps A2 of acquiring the spectrum of the known        sample;    -   one or more steps A3 of processing the spectrum of the known        sample.

In some embodiments, said steps A1, A2, A3 can be repeated every time anew sample known in the database is to be inserted.

In some embodiments, the sampling steps B, A1 of a sample can providethat the sample is taken from a patient.

In some embodiments, the sample taken from the patient can be subjectedto preliminary analysis procedures, which provide to add nutrientsubstances for the microorganisms.

In some embodiments, the sample can be grown on a solid culture medium.

In some embodiments, the sample can be grown on Petri dishes.

In some embodiments, the sample can be grown on liquid culture media andthen centrifuged, obtaining a concentrated pellet.

In some embodiments, the sample can be grown on liquid culture media, orliquid growth broth, and then filtered.

In some embodiments, the sample can be grown on liquid culture media, orliquid growth broth, or it can be subjected to concentration orenrichment procedures to increase the concentration of microorganismspresent.

In some embodiments, the sample can possibly contain biological fluids,of human or animal origin.

In some embodiments, the spectrum acquisition steps C, A2 can provide apreliminary step of cleaning the surfaces of the apparatus in contactwith the sample, for example of the acquisition zone 18 shown in FIG. 1.

In some embodiments, it is possible to acquire background spectra, forexample by means of continuous acquisition mode, to verify theeffectiveness of such cleaning, for example by observing thedisappearance of absorption bands linked to the impurities deposited onthe acquisition zone 18.

In some embodiments, the spectrum acquisition steps C, A2 also providethat an assay of the sample is taken and deposited on the crystal 12 ofthe spectrophotometer, for example on the acquisition zone 18, in orderto acquire the spectrum.

In some embodiments, the assay is deposited on the acquisition zone 18in a solid form, for example by removing and depositing it by means of adisposable rod.

In some embodiments, the assay can be pressed against the acquisitionzone 18, for example by using a dynamometric press.

In some embodiments, the level of drying of the sample isspectroscopically monitored.

In some embodiments, it is possible to evaluate the water contentleaving the apparatus in continuous acquisition mode and monitoring thereduction, up to a possible complete disappearance, of the absorptionbands relating to the spectral characteristics of the water in therespective ranges of wave numbers.

According to the present invention, the spectrum is acquired when apredetermined standard level of drying of the sample is reached.

Advantageously, this characteristic allows to overcome or at least limitthe problem of the state of the art whereby the spectra can have signalsdue to the presence of water that cover, or interfere with, the peculiarsignals of the microorganisms.

This characteristic also allows to avoid procedures for drying thesample that could irreversibly modify the microorganisms to be analyzed.

The spectrum acquisition steps C, A2 also provide that the spectrum ofthe assay taken from the sample is recorded.

In some embodiments, the spectrum is recorded for a predetermined periodof time.

In some embodiments, the spectrum is recorded for a period of time ofless than 30 seconds.

In some embodiments, spectrum recording provides to acquire apredetermined number of sequential spectra, which are then averaged toimprove the signal to noise ratio.

In some embodiments, a number of spectra comprised in a range of 8 to512 for each assay can be acquired and averaged, preferably between 32and 256 for each assay, even more preferably between 64 and 128 for eachassay.

In some embodiments, the spectrum, or possibly the average of thespectra, is displayed on the screen.

The steps D, A3 of processing the spectrum, or of the average of thespectra, can provide:

-   -   to use linear and/or non-linear interpolation and/or fitting        algorithms (spectral profile);    -   to calculate the first and/or second derivative of the spectral        profile;    -   to normalize the derivatives using a vector normalization        algorithm over the entire spectral range;    -   to select the most useful spectral zones for the classification        of the species, subspecies or sub-classifications of        microorganisms.

By way of example, the spectral zones can comprise: the range 950-1280cm⁻¹ for nucleic acids, carbohydrates and polysaccharides; 1280-1480cm⁻¹ for starches, for example proteins, methyl and methylenes, forexample lipids; 1700-1800 cm⁻¹ for the carbonyl groups, for example oflipids; 2800-3000 cm⁻¹ for aliphatic chains, for example lipids.

In some embodiments, artificial learning algorithms (machine learning)can be used to improve the selection process of the spectral ranges.

By way of example, FIG. 3a shows the spectra that can be collected fordifferent samples, while FIG. 3b shows the processed spectra.

FIG. 3a also shows by way of example some identification spectralcharacteristics significant for the purposes of the method of thepresent invention.

In some embodiments, the spectral regions and identification spectralcharacteristics are then used to classify and subsequently identify theindividual species, subspecies or sub-classifications of microorganisms.

In some embodiments, an automatic recognition system is provided, toidentify the spectral ranges and the identification spectralcharacteristics of greatest interest.

Advantageously, the presence of different chemical classes, such as forexample nucleic acids, lipids, proteins, carbohydrates, and otherconstituents of microorganisms, allows to obtain characteristic signalsfor each species, subspecies or sub-classification of microorganisms, sothat it is possible to obtain a predictive method.

By repeating steps A1, A2, A3 for several known samples, it is possibleto prepare the database of reference spectra of species, subspecies orsub-classifications of known microorganisms.

However, in some embodiments of the present method it is also providedthat the database can comprise spectra of species, subspecies orsub-classifications of known microorganisms obtained using othermethods.

In some embodiments, the database comprises spectra relating tomonomicrobial cultures belonging to different species, subspecies orsub-classifications of known microorganisms.

In some embodiments, the database comprises spectra relating todifferent strains of known microorganisms for each species, subspeciesor sub-classification of known microorganisms.

In some embodiments, the database comprises spectra relating to samplesgrown on different culture media, for example but not only Agar, CNAAgar, CLED Agar, Blood Agar, Chromogenic Agar.

In some embodiments, the database comprises spectra relating to samplesgrown in a liquid-phase growth broth, which are then centrifuged, orfiltered, or enriched, to obtain pellets or concentrated samples.

Advantageously, the spectra obtained by growth in liquid broth andsubsequent pelletizing or filtration, can be used as standard referencespectra, since they do not contain interferences deriving from matricesof solid-phase growth media.

Advantageously, measurements of spectra obtained from growth in liquidbroth and subsequent pelletizing or filtration or enrichment allow toidentify the spectra directly in the presence of biological fluids, forexample bodily fluids.

This heterogeneity and variability of the spectra contained in thedatabase allows to perform a general analysis, which allows to identifymany species, subspecies or sub-classifications of microorganisms, manydifferent strains for each individual species, subspecies orsub-classification, also including effects deriving from growths ondifferent culture media.

In the embodiments where multiple acquisitions of each spectrum areused, it is possible to insert in the database the individual spectrarepeated and/or the average spectrum calculated on the repetitions.

The pre-calculated models, or reference libraries, created or updated instep A4, can be based on the most significant identification spectralcharacteristics for the purposes of the analysis in the spectral rangesselected.

In some embodiments, the most significant identification spectralcharacteristics can be associated, for example, with information onshapes, sizes, intensities and areas of absorption peaks, or alsocorrelations and ratios between the intensities or between the areas ofdifferent peaks.

In some embodiments, the pre-calculated models can comprise a list ofidentification spectral characteristics significant for each knownsample of species, subspecies or sub-classification of knownmicroorganisms.

In some embodiments, the most significant identification spectralcharacteristics can be identified by said statistical analysistechniques, and/or approaches based on neural networks and/or artificiallearning.

These pre-calculated models allow to quickly and automatically identifythe most significant identification spectral characteristics for thepurposes of comparison.

In this way, every time two spectra are compared, the comparison can beperformed only in the spectral ranges and/or in relation to the spectralcharacteristics of greatest interest, rather than on the whole spectralrange and/or for all the spectral characteristics.

For example, if one wanted to compare, over the entire spectral range, aspectrum of an unknown sample acquired with a resolution of 1 cm⁻¹, witha database that contains, by way of example, 18000 reference spectraacquired with the same resolution, this comparison, using the methods ofthe state of the art, would require the evaluation of 65 million points.

Applicant has found that, by using the methods according to the presentinvention, working on selected spectral ranges and with thepre-calculated models, this comparison can require a number ofevaluations of points between 20 and 80 times lower than the methods ofthe state of the art.

Using pre-calculated models therefore allows to significantly reduce theanalysis times for each unknown sample, even in the presence of largedatabases.

This characteristic therefore allows to considerably increase the sizeof the databases to comprise a high number of species, subspecies orsub-classifications of known microorganisms, thus improving andextending the predictive and identification capabilities of the methodof the present invention.

This characteristic also allows to work with spectra even at highresolution, improving the accuracy and precision of the method.

Moreover, for example, the presence of the list can make it superfluousto use least-squares methods and/or possible calculations of averagesquare discards on the whole range of wave numbers for all the spectra.

In other embodiments, it is provided to use artificial learning (machinelearning) and artificial intelligence models, to improve the automaticrecognition system and the pre-calculated models, progressively as themethod is used.

In some embodiments, the analysis step E provides that the spectrum ofthe unknown sample is compared with reference spectra contained in thereference database and/or with pre-calculated models.

In some embodiments of the present invention, statistical techniques canbe used for this comparison.

In some embodiments of the present invention, the statistical techniquescan comprise multivariate analysis, for example Principal ComponentsAnalysis (PCA), or Linear Discriminant Analysis (LDA), also LinearDiscriminant partial least-squares (LDPLS), Quadratic DiscriminantAnalysis (QD).

In some embodiments, methods, techniques or algorithms that implementapproaches based on neural networks can be used for said comparison.

In some embodiments, methods, techniques or algorithms that implementapproaches based on artificial learning (machine learning) can be usedfor said comparison.

In some embodiments of the present invention, said comparison can alsoprovide to compare the derivatives, first and/or second, of the spectra.

In some embodiments, it is possible to use statistical weights to weighdifferent regions of the spectra in different ways.

In some embodiments of the present invention, the comparison between twospectra can be performed by means of a definition of distance betweentwo spectra, for example using methods based on least-squares.

In some embodiments, the distance can be used as a metric to performstatistical and/or chemometric analyses.

In some embodiments, the variance between spectra can be used for thePCA analysis.

In some embodiments, the analysis step E provides a score, for example apercentage, of the unknown sample belonging to one or more of thespecies, subspecies or sub-classifications, represented in the database.

In some embodiments, the analysis step E provides a reliabilityparameter that estimates the reliability of the analysis, and inparticular the belonging score.

In some embodiments, the control step F provides to process thebelonging score and the reliability parameter.

According to some embodiments, a belonging score is consideredimmediately reliable if the reliability parameter of the analysis ishigher than a pre-set level of acceptability. For example, if percentagescores are used, the level of acceptability can be chosen in a rangecomprised between 80% and 100% (block G in FIG. 2).

In some embodiments, if it is not possible to identify the unknownspecies, subspecies or sub-classification of microorganisms in anunequivocal manner at the first attempt, it is possible to repeat stepsC, D, E and F to add new data and improve the result of the analysis.

In some embodiments, this can be provided if the reliability parameteris lower than a second preset level of acceptability. For example, inthe embodiments that use scores expressed as a percentage, this secondlevel can be selected in a range comprised between 70% and 80%.

In this case, the method provides a second acquisition step C, a secondprocessing step D and a second analysis step E to be carried out on asecond assay taken from the unknown sample.

For example, the second assay can be a different colony ofmicroorganisms, taken from the same Petri dish on which the unknownsample was cultivated and grown.

If the sample is in pellet form, the second assay can be taken, forexample, with a disposable rod from the same concentrated pellet fromwhich the first one was taken.

Afterward, if the reliability of the result of the second analysis isagain lower than a third preset level of acceptability, for examplenumerically equal to the second level of acceptability, the methodprovides to compare the similarity between the two spectra obtained withthe first acquisition step C and with the second acquisition step C.

If the spectra are found similar (block H in FIG. 2), the methodsupplies, as the result of the analysis, the belonging scores of theunknown sample to one or more species, subspecies or sub-classificationsof microorganisms, in agreement between the two acquisitions.

If the spectra are found to be discordant, it is provided that a thirdassay is taken, and a third acquisition step C, a third processing stepD and a third analysis step E are performed.

If at least two of the three spectra are found similar (block I in FIG.2), the method provides, as the result of the analysis, the belongingscores of the sample to the species, subspecies or sub-classificationsof the microorganisms of the database, in agreement with two of thethree spectra.

Otherwise (block J in FIG. 2), a message of non-identification isdisplayed, together with the spectra and the three belonging scoresrelating to the three acquisitions.

One embodiment provides a computer program, or software, used to collectthe data and analyze them with respect to the database, which can bememorized in a computer-readable medium and which contains instructionswhich, once executed by an analysis apparatus for the classification andidentification of microorganisms, determine the execution of the methodaccording to the present description.

Example 1

By way of example, FIG. 4 shows schematically the results of a PCAanalysis of the spectra contained in a database in accordance with thepresent invention.

In particular, each point in FIG. 4 corresponds to the spectrum, or tothe average of the spectra acquired, relating to a species, subspeciesor sub-classifications of microorganisms, oriented along the first twoprincipal components.

The spectra have also been grouped into zones, the extension of which isconnected to the variability of the spectra of each species, subspeciesor sub-classifications of microorganisms, based on the different strainsand/or different culture media for each sample.

Example 2

By way of example, FIG. 5 schematically shows the results of an LDAanalysis of the spectra contained in a database according to the presentinvention.

In particular, each point in FIG. 5 corresponds to the spectrum, or tothe average of the spectra acquired, relating to a species, subspeciesor sub-classifications of microorganisms, oriented along the first twocomponents.

The spectra have also been grouped into zones, the extension of which isrelated to the variability of the spectra of each species, subspecies orsub-classifications of microorganisms, based on the different strainsand/or different culture media for each sample.

Example 3

In one embodiment, a database according to the present invention hasbeen obtained by sowing selected and certified NNCCLS type Atlantamicroorganisms in Petri dishes or on liquid medium.

In this embodiment, specific spectra were obtained for each individualmicroorganism sown on a Petri dish or on liquid medium; moreover, eachsingle strain was sown in various growth media, using Petri dishes fromvarious producers of liquid media, in order to verify the variability ofthe spectra with respect to the growth conditions.

The media used was selected from:

-   -   CLED Agar    -   MacConkey Agar    -   CNA Agar    -   Blood agar

For each microorganism, 500 growths of microorganisms were obtained inorder to have a broad and complete case study with regard to obtaining anumerical cluster able to verify the correlation of the data comparedwith other methods in use.

FIG. 6 represents the data acquired in the space of the first twoprincipal components in which each species is represented by a differentshade of gray.

Example 4

In one embodiment, the method according to the invention can be used topredict the GRAM-type microorganism. An example of the results on theGRAM+GRAM classification—is shown in FIG. 7.

Panel a) in FIG. 7 is a representation of the PCA space colored aspositive GRAM (light gray) and negative GRAM (dark gray).

Panel b) in FIG. 7 is the confusion matrix showing the “predicted” datacompared with the “actual” data obtained from the method.

Panel c) in FIG. 7 shows the performance results of the mathematicalmethod in predicting the GRAM type.

Example 5

In one embodiment, the method can be used to identify species,subspecies or sub-classifications of microorganisms. See FIG. 8, whichshows a confusion matrix obtained from the validation of the method withthe spectra of a database according to the present invention, in which acorrespondence from 99.9% to 96% can be observed between the actual andthe predicted.

It is clear that modifications and/or additions of parts or steps can bemade to the method and/or apparatus as described heretofore, withoutdeparting from the field and scope of the present invention. It is alsoclear that, although the present invention has been described withreference to some specific examples, a person of skill in the art shallcertainly be able to achieve many other equivalent forms of methodand/or apparatus, having the characteristics as set forth in the claimsand hence all coming within the field of protection defined thereby.

The invention claimed is:
 1. A method to identify microorganisms in abiological sample, using an infrared spectroscope with attenuated totalreflectance comprising: a step of preparing a database of referencespectra associated with known samples of species, subspecies andsub-classifications of known microorganisms; one or more steps ofcreating pre-calculated models, starting from said database, based onidentification spectral characteristics most significant for eachspecies, subspecies or sub-classification of known microorganisms; astep of sampling an unknown sample, obtained from growth on solid orliquid media, possibly with biological fluids; one or more steps ofacquiring the spectrum of the unknown sample; one or more steps ofprocessing the spectrum of the unknown sample; one or more steps ofanalyzing the spectrum of the unknown sample, by comparing it with thepre-calculated models, each of which provides at least some belongingscores of the unknown sample to one or more of said species, subspeciesor sub-classifications of known microorganisms and a parameter of thereliability of said belonging scores; one or more control steps inwhich, starting from said belonging scores and from said reliabilityparameter, a new acquisition step of the spectrum of the unknown sampleis requested, or a final result of the method is provided, which cancomprise a failed identification, or a successful identification;wherein the acquisition steps provide spectroscopic monitoring of thedrying level of the sample, so that the spectrum is acquired only when apredetermined standard level of drying is reached.
 2. The method as inclaim 1, wherein the steps of preparing a database of referencespectrums associated with known sample species, subspecies orsub-classification of known microorganisms provides, for each knownsample: a step of sampling the known sample obtained by growth on solidor liquid media in the presence or absence of biological fluids; one ormore steps of acquiring the spectrum of the known sample; one or moresteps of processing the spectrum of the known sample.
 3. The method asin claim 1, wherein the reference spectra comprise spectra associatedwith samples of different species, subspecies or sub-classifications ofmicroorganisms and/or of different strains of the same species,subspecies or sub-classifications of microorganisms and/or grown onvariable culture media, with or without biological fluids.
 4. The methodas in claim 1, wherein the unknown sample is grown on a solid culturemedium.
 5. The method as in claim 1, wherein at least one of the knownsamples is grown on a solid culture medium.
 6. The method as in claim 1,wherein the unknown sample is grown in at least one growth liquid broth,subsequently centrifuged or filtered or enriched to obtain a pellet or aconcentrated sample, and in that the spectrum is acquired by removingassays from said pellet or concentrated sample.
 7. The method as inclaim 1, wherein at least one of the known samples is grown in at leastone growth liquid broth, subsequently centrifuged or filtered orenriched to obtain a pellet or a concentrated sample, and in that thespectrum is acquired by removing assays from said pellet or concentratedsample.
 8. The method as in claim 1, wherein the processing stepprovides to use algorithms which automatically identify the mostsignificant identification spectral characteristics of the samples inspectral ranges defined in advance.
 9. The method as in claim 1, whereinthe analysis step provides to use statistical methods such asmultivariate analysis.
 10. The method as in claim 1, wherein theanalysis step provides to use methods based on the analysis of theprincipal components.
 11. The method as in claim 1, wherein the analysisstep provides to use methods based on neural networks.
 12. The method asin claim 1, wherein the analysis step provides to use methods based onthe analysis of clusters of spectra.
 13. The method as in claim 1,wherein said pre-calculated models comprise a list of saididentification spectral characteristics for each sample, both unknownand known.
 14. An apparatus to carry out the method to identifymicroorganisms in a biological sample as in claim 1, comprising aspectrophotometer for FTIR spectroscopy in ATR acquisition mode, coupledwith a processing system and a data display system installed in acomputer, a source of infrared radiation, reflecting elements, aninternal reflection element, such as a crystal, and a detector, anacquisition zone being defined on the surface of said crystal on whichthe biological sample to be analyzed is positioned, wherein in saidcomputer said pre-calculated models associated with known samples ofspecies, subspecies and sub-classifications of known microorganisms arememorized.